PSO-BASED RESOURCE SCHEDULING ALGORITHM

FOR PARALLEL QUERY PROCESSING ON GRIDS

Arturo Pérez-Cebreros, Gilberto Martínez-Luna and Nareli Cruz-Cortés

Database and Information Systems Laboratory, Center for Computer Research

National Polytechnic Institute, Av. Juan Dios Batiz s/n, Zacatenco 07738, Mexico City, Mexico

Keywords: Particle Swarm Optimization, Genetic Algorithms, Grid, Database, Query Scheduler.

Abstract: The accelerated development in Grid computing has positioned it as promising next generation computing

platforms. Grid computing contains resource management, task scheduling, security problems, information

management and so on. In the context of database query processing, existing parallelisation techniques can

not operate well in Grid environments, because the way they select machines and allocate queries. This is

due to the geographic distribution of resources that are owned by different organizations. The resource

owners have different usage or access policies, cost models, varying loads and availability. It is a big

challenge for efficient scheduling algorithm design and implementation. In this paper, a heuristic approach

based on particle swarm optimization algorithm is adopted to solving parallel query scheduling problem in

grid environment.

1 INTRODUCTION

The emerging paradigm of grid computing and the

construction of computational grids are making the

development of large scale applications possible

from optimization and other fields (Foster, 1998).

However, this have increased the necessity of novel

applications that require close and potentially

sophisticated interaction and data sharing between

resources that may belong to different organizations.

Examples include the bio-informatics labs across the

world sharing their simulation tools, experimental

results; as well as the use of the donated spare

computer time of thousands of PCs connected to the

Internet in order to solve computation problems.

Hence, the question is how database management

systems and technologies can best be deployed or

adapted for be used in such environments. As a

consequence of this, the databases technologies have

led to many proposals that try to integrate databases

with Grid applications (Alpdemer, 2003; Liu, 2003;

Narayanan, 2003). In particular, query processors for

Grid-enabled databases, such as (Alpdemer, 2003;

Smith, 2003) can provide effective declarative

support for combining data access with analysis to

perform non-trivial tasks, and are well suited for

intensive applications as they naturally care for

parallelism. This is due to the fact that many

complicated tasks can be effectively encapsulated

and specified by databases queries. However, one of

the more difficult tasks for the efficient exploitation

of parallelism in such query processors; it is

scheduling a large number of queries, in order to

achieve better performance. For the reason that

many computing resources are geographically

distributed under different ownerships, each having

their own accesses policy, cost, and various

constraints. Unlike scheduling problem in

distributed systems, this problem is much more

complex as new features of Grid systems such as its

dynamic nature and the high degree of heterogeneity

of tasks and resources must be tackled.

Systems like OGSA-DQP are capable of

performing query processing over heterogeneous

local database management systems, like mySQL

and Oracle. Such systems may employ a number of

machines to run the query processing tasks. Clearly,

these machines can be heterogeneous as well, in

terms of their computational capacity and

characteristics. For instance, some may have high-

speed interconnections, interfaces, operating

systems, a larger amount of memory or a low power

CPU. This kind of heterogeneity environment (HE)

has motivated our project.

131

Pérez-Cebreros A., Martínez-Luna G. and Cruz-Cortés N. (2009).

PSO-BASED RESOURCE SCHEDULING ALGORITHM FOR PARALLEL QUERY PROCESSING ON GRIDS..

In Proceedings of the 11th International Conference on Enterprise Information Systems - Databases and Information Systems Integration, pages

131-137

DOI: 10.5220/0001986901310137

 SciTePress

In this paper, the Particle Swarm Optimization

(PSO) is employ to solve the scheduling problem in

Grid enabled databases environment. The

practicality of the approach lies in the fact that

resource scheduling remains NP-complete even

when an unlimited number of processors is available

(Chrétienne, 1992), as a logical consequence of the

NP-completeness of scheduling, the scientific

community has been eager to investigate efficient

scheduling algorithms based on heuristics or

approximation techniques that produce near optimal

solutions. In practice, scheduling must rely on these

algorithms due to the intractability of the problem.

This paper is organized as follows. We present

the problem in Section 2. A Particle Swarm

Optimization (PSO) is introduced in Section 3. We

compare our work with others in Section 4.

Experiment settings and results are discussed in

Section 5 and some conclusions are given in Section

2 PROBLEM DESCRIPTION

It is well know that, even in homogeneous systems,

choosing the maximum degree of parallelism not

only harms the efficiency of resource utilization, but

can also degrade the system's performance

(Wilschut, 1992). Therefore, on Grid enabled, it

holds as well.

Furthermore, the problem of query partition

parallelism is discussed under the viewpoint of

sending query fragments to many databases and

executing the query in parallel. So the main focus is

on executing a query fragment at any database as

long as the cost for query execution is minimal.

Therefore, it is clear that some optimizations and

restrictions for data access and query execution are

required. Unfortunately, finding a schedule of

minimal length is in general a difficult problem. This

becomes intuitively clear as one realizes that an

optimal schedule is a trade-off between high

parallelism and low inter-machine communication.

On the one hand, query fragments should be

distributed among the machines in order to balance

the workload. On the other hand, the more the query

fragments are distributed, the more inter-machine

communications.

Another problem of resource selection for a

query scheduler on emergent Grids is the budget; the

integration of computational economy as part of

scheduling system greatly influences the way

computational resources are selected to meet the

user requirements. The users should be able to

submit their queries along with their requirements to

a scheduling system.

Moreover, many of the resource management

systems (e.g. (Stonebraker, 1994; Heiser, 1998;

Amir, 2000; Buyya, 2000) ) support a single model

for resource trading, provide their own programming

model and are implemented as monolithic systems.

To overcome these limitations, the modern Grid

computing systems use a layered architecture. Users

and owners's resources have their own expectations

and strategies for being part of the Grid (Buyya,

2001). In particular, the resource consumers adopt

the strategy of solving their problems at low cost

within a required time frame. The resource providers

adopt the strategy of obtaining best possible return

on their investment while trying to maximize their

resource utilization by offering a competitive service

access cost in order to attract consumers.

Indeed, the grid enabled database environment is

dynamic and, also the number of resources to

manage and the number of queries to be scheduled.

These queries are usually very large making thus the

problem a complex large scale optimization problem

(as several optimization criteria such as response

time and/or economic cost must to be matched).

3 PSO QUERY SCHEDULER

ON GRIDS

3.1 Solution Approach

The complexity of the process of resource selection

and creating a schedule S for a query graph G on a

set of databases D is a key problem in emergent

computational Grids. It should be obvious that

generally there is more than one possible schedule

for a given graph and a set of databases (which of

course would have consequences for the scheduling

of the other queries). Due that the usual purpose in

employing a parallel system is the fast execution of a

program; the usual aim is to produce a schedule of

minimal length.

The algorithm proposed here receives a query

plan which can be partitioned into sub-plans that can

be evaluated on different machines. In our

simulation, we assume the existence of a query

optimizer which first constructs a single-node plan,

and then transforms the single-node plan into a

multi-node one, in order to reduce the search space

(Kossman, 2000).

For example, exchange operators encapsulate the

parallelism and involve communication (Graefe,

ICEIS 2009 - International Conference on Enterprise Information Systems

132

1990), and they naturally define the boundaries of

different sub plans. Let us assume there are two

databases, each containing one table, namely Orders

and LineItem. The nodes of the plan are query

operators (scans, projects, joins, and exchanges or

data communication). These operators may be

executed on different machines in parallel. But we

assume that scans are not parallelisable, because

data from any single table is accessed in an existing

database. So suppose that the optimizer decides,

using PSO Query Scheduler proposed here, that the

join of this example, implemented as a hash join

algorithm, should be cloned at any site, in order to

deal with the second stage, where the single node

plan is parallelized and how to improve it.

In particular, in grid-enabled databases

considered here, a query is decomposed into a set of

partitions S. Define |S| to be the number of partitions

in the set S and si to be the ith partition, then S = {s

0 ≤ i < |S|}. A grid-enabled database consists of a set

of heterogeneous databases D. Define |D| to be the

number of databases in the set D an m

to be the jth

database, then M = {m

, 0 ≤ j < |D|}. The estimated

expected execution time of partition si on database

is T

, where 0 ≤ i < |S| and 0 ≤ j < |D|.

To formulate this problem under our simulation

model, an estimation of the computational load of

each partition, the computing capacity of each

resource, and an estimation of the prior load (CPU,

Network) of each one of the resources are required.

Thus, we make the usual assumption that we know

the computing capacity of each resource, the

estimation or prediction of the computational

necessities (workload) of each partition, and the load

of prior work of each resource. This assumption is

typically made for the current state-of-the-art in

Heterogeneous Computing systems when studying

the matching and scheduling problem (Freund, 1994;

Singh, 1996; Shroff, 1996). Finding the estimated

expected execution times for subtasks is another

research problem, which is outside the scope of this

paper.

3.2 Particle Swarm Optimization

PSO is a population-based search algorithm and is

initialized with a population of random solutions,

called particles (Hu, 2004). Unlike in other

evolutionary computation techniques, each particle

in PSO is also associated with a velocity. Particles

fly through the search space with velocities which

are dynamically adjusted according to their

historical behaviour.

In particular, PSO learns the scenario and uses it

to solve optimization problems. Thus, each single

solution is like a 'bird' in the search space, which is

called 'particle'. All particles have fitness values

which are evaluated by the fitness function to be

optimized, and have velocities which direct the

flying of the particles. So the particles fly through

the search space by following the particles

(solutions) and then searches for optima by updating

each generation, following equations will be used to

compute the new elements of velocity and position

vectors:

(k+1)=X[w

(k)v

(k)+c

(k)-

(k)]+ c

(k)-x

(k)]]

(1)

(k+1)=x

(k)+v

(k+1) (2)

where wk is the inertia weight, which represents the

particle's preference to continue moving in the same

direction it was going on the previous iteration,

introduced in (Shi, 1998), X is the constriction

coefficient, which serves as a balancing factor for

the local and global search, introduced in (Clerc,

2002), c1 and c2 are cognitive and social factors

respectively, often set equal to 2, k represents the

iteration number, r1 and r2 are random numbers

between [0,1], i(i=1,2,...,N) is the index representing

the particles in the swarm and d (d=1,2,...n) is the

index for dimensions of searching space. We define

one particle as a possible solution in the population,

and the fitness function is the minimum time/cost

value. The main PSO algorithm is given below:

begin

for i=1 to number of particles do

Initialize position and

velocity randomly;

Initialize the neighborhood;

end

repeat

compute the fitness value

G(x

);

for i=1 to number of

particles do

if G(x

)>G(xpb

) then

for d=1 to number

of dimensions do

xpb

;

end

select the local best

position in the

neighborhood lb

;

PSO-BASED RESOURCE SCHEDULING ALGORITHM FOR PARALLEL QUERY PROCESSING ON GRIDS.

133

for d=1 to number of

dimensions do

w=rand();

=w * v

cir1(pbx

-x

) +

c2r2(lb

-x

);

;

end

perform mutation;

until Maximum number of

generations;

scheduling resources;

end

From a higher level point of view our approach

transform an existing plan to a more efficient, by a

PSO technique, modifying the set of resources

allocated to a part of the query plan.

Transformational approaches to query optimization

have already been employed for constructing query

plans (Ioannidis, 1996), however not for scheduling

resources.

4 RELATED WORK

There is one previous work (Gounaris, 2006) that

deals with the scheduling of resources in

heterogeneous environments to support arbitrary

degrees of partition parallelism; however, it

mitigates the problem of devising a near optimal

workload distribution of tuples among the selected

databases. In other words, they assume that all the

available machines are similar in terms of workload,

so they simplified the problem of resource

scheduling by neglecting the problems of a near

optimal distribution due to its NP-hard complexity.

In the literature, different approaches can be

found that relate to ours in certain ways. Due to its

theoretical and practical relevance, the evolutionary

computing research community has started to

examine the Grid resource scheduling (Di, 2004;

Abraham, 2000; Zomaya, 2001; Meijer, 2004;

Braun, 2001). However, the existing approaches in

the literature show several limitations: in some

works just the uni-objective case is considered and

usually either concrete grid environments.

Moreover, the schedulers's performance has been

studied only on small size instances. Dynamic

aspects of this problem have not been addressed so

far to Grid enabled databases environments.

Particularly in the database field, distributed

query processing has mostly been influenced by

some pioneering systems. The most influential,

System R* (Mackert, 1986), but they simplified the

problem of resource scheduling by neglecting the

benefits of partitioned parallelism. In other words,

the data are retrieved from a single site only, and are

joined on a single site, which is either the site of one

of the inputs or the site that asked for the data.

Distributed Ingres (Epstein, 1978) took a step

forward; they used a fragment and replicate query

processing strategy. A query is partitioned into

equal-sized fragments, each fragment is sent to a

computer processor, and all other relations are

replicated in all computer processors, however,

different machines may have different processing

speeds and/or different access methods for accessing

required data, thus equal-distribution of fragments

may lead to load imbalance. In (Rahm, 1995)

discusses an approach for load balancing employing

partitioned parallelism, although it refers to

completely homogeneous environments, it does not

force the system to employ all the available nodes

when there are not needed. Other existing techniques

for parallel and distributed databases (Garofalakis,

1997; Mayr, 2003, DeWitt, 1986) do not consider

partitioned parallelism or completely ignore the

resource selection phase by assuming a fixed set of

resources then they try to schedule tasks over these

resources. In other words, they also assume

homogeneous and stable environments in terms of

capabilities, connection speed and ownership.

5 EXPERIMENTAL SETTINGS

AND RESULTS

For the evaluation of our proposal; we built a

simulator by extending the cost model in (Sampaio,

2002), which is a detailed and validated simulator

developed for parallel object database systems, it has

been adapted to operate in a heterogeneous and

autonomous environment and has been incorporated

in the query engine.

In our simulation, we define 156 cost models,

each one is a database; each one estimates the

response time by estimating the cost of each

operator instance separately in time units. The

communication costs are also considered; these costs

are composed of fixed costs per message, per-byte

costs to transfer data, and CPU costs to pack and

unpack tuples. Each database in our simulation is

heterogeneous because we change several system

parameters, which are described in Table 1.

We assumed that the processing speeds of each

machine/resource, budget(G$) and network of each

ICEIS 2009 - International Conference on Enterprise Information Systems

134

query are known. Hence, Expected Time to

Compute matrix (ETC) is updated by the time units

that are generated by our cost models; where the

position ETC[i][j] indicates the expected execution

time of partition i in machine m. This ETC matrix is

needed in order to compute our fitness function.

Table 1: System parameters.

Description Unit

Seek time of disk s

Time to probe hash table s

Size of the network packet bytes

Network bandwidth Mb/s

Size of the exchange producer cache tuples

Size of the exchange consumer cache tuples

Time to insert into hash table tuples

Table 2: Tables from TPC-H.

Name Short Name Cardinality Tuple Size

Part P 200,000 159

PartSupp PS 800,000 144

Orders O 1,500,000 104

LineItem L 6,000,000 112

Supplier S 10,000 159

Table 3: Parameter settings of PSO and GA algorithm.

Name Parameter

description

Parameter value

PSO Size of swarm 100

Self-recognition

coefficient (c1)

Social coefficient

(c2)

Weight (w) 0.9 0.4

Max velocity 100

GA Size of population 100

Probability of

crossover

0.8

Probability of

mutation

0.03

Scale for

mutations

0.1

For the experiment, we used a variety of queries,

which makes relations from Table 2, and all queries

make use of the TPC-H database. Finally, we totally

submitted 4000 queries consecutively to the Grid in

two stages; and the computational complexity of all

these queries was randomly chosen (about three to

five joins). In first stage, we submitted 2000 queries

to the Grid and the queries were scheduled by our

PSO Query Scheduler algorithm. After the first 2000

queries were all finished, we submitted the second

2000 queries that were allocated with the Genetic

Query Scheduler (Di, 2004).

Thus, we compared the performance of PSO

algorithm with Genetic algorithm (GA) that has

many similarities. The experimental parameter

settings of PSO and GA algorithms are described in

Table 3.

Figure 1: Total no. of queries in execution during time

optimization strategy.

Figure 2: Total no. of queries in execution during cost

optimization strategy.

In our experiments, this randomly generated

scenario was used for two reasons: (1) it is desirable

to obtain data that demonstrate the effectiveness of

the approach over a broad range of conditions, (2) it

is not clear what characteristics a “typical”

heterogeneous computing queries would exhibit. So

we conducted experiments for two different

optimization strategies:

1. Optimize for time (produce results as early

as possible).

2. Optimize for cost (produce results by

deadline, but reduce cost)

The number of queries in execution on resources

(Y-axis) at different times (X-axis) during the

experimentation is shown in Figure 1 and 2 for Time

and Cost Optimization strategies respectively.

For example, in bioinformatics databases, this

provides consumers the ability to trade-off between

time frame and cost that they would like to invest for

solving the problem in hand. When the deadline is

too tight and results are needed at the earliest

possible time, then consumers should be prepared to

spend more money. This can be shown in Figure 3,

PSO-BASED RESOURCE SCHEDULING ALGORITHM FOR PARALLEL QUERY PROCESSING ON GRIDS.

135

the amount of budget consumed by Cost

Optimization strategies.

Figure 3: The total amount spent during cost optimization

strategy.

6 CONCLUSIONS

Grid computing technologies are enabling the

creation of virtual enterprises for sharing distributed

resources and changing the way we compute,

communicate, and interact with database systems

and people. Current distributed database applications

operating in heterogeneous settings, like

computational Grids, tend to run queries with a sub

optimal degree of partitioned parallelism, with

negative consequences for performance when the

queries are computation and data intensive. On the

fly creation of Internet-scale virtual computing

environments is becoming more of a reality than

dream. Hence, the system managing resources in this

complex environment need to be smart, adaptable to

changes in the environment and user requirements.

At the same time, they need to provide a scalable,

controllable, measurable, and understandable policy

for management resources. The main contribution of

this work is the proposal of PSO resource scheduler

that allows a near optimal degree of partitioned

parallelism so as to complete the queries in a

minimum time as well as utilizing the resources in a

computational economy approach and compare it

with genetic algorithm under the same condition. To

the best of our knowledge, this is the first such

proposal.

From the simulated experiment, the results

demonstrate that PSO algorithm can get better effect

for a large scale optimization problem. Nowadays

we are working with more complex scenarios, such

as scenarios with changing levels of contention.

Finally, another research direction is to create

different heuristic based algorithms for problems

arising in grid computing.

ACKNOWLEDGEMENTS

The first author gratefully acknowledges support

from CONACyT through a scholarship to pursue

studies at CIC-IPN’s Computer Science Department.

The second author acknowledges support from SIP-

IPN through project 20091245 and 20091317. The

third author acknowledges support from SIP-IPN

through project 20091712.

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